A DATA-DRIVEN APPROACH TO PLANT SUGGESTIONS USING SOIL HEALTH AND FOOD PRICE DATA

This research introduces a data-driven framework for suggesting suitable plants by analyzing soil health and food prices. It aims to enhance agricultural decision-making by integrating these factors. Through data collection, preprocessing, and feature engineering, a predictive model is developed, correlating soil attributes and market costs with plant performance. This model aids in identifying optimal crops for specific locations. By omitting the user interface aspect, the focus remains on technical aspects. The project holds potential for optimizing crop choices, leading to improved agricultural yield and economic outcomes.